seamless_interaction/README.md

Seamless Interaction Dataset

A large-scale multimodal dataset of 4,000+ hours of human interactions for AI research

🖼️ Blog

🌐 Website

🎮 Demo

🤗 HuggingFace

📄 Paper

Overview

Human communication involves a complex interplay of verbal and nonverbal signals, essential for conveying meaning and achieving interpersonal goals.

The Seamless Interaction Dataset is a large-scale collection of over 4,000 hours of face-to-face interaction footage from more than 4,000 participants in diverse contexts. This dataset enables the development of AI technologies that understand human interactions and communication, unlocking breakthroughs in:

• 🤖 Virtual agents and embodied AI
• 🎭 Natural human-computer interaction
• 📡 Advanced telepresence experiences
• 📊 Multimodal content analysis tools
• 🎬 Animation and synthetic content generation

🚀 Quick Start

git clone https://github.com/facebookresearch/seamless_interaction
cd seamless_interaction
pip install -e .
streamlit run src/seamless_interaction/app/Welcome.py

# if you use uv
uv sync
uv run streamlit run src/seamless_interaction/app/Welcome.py

Explore the dataset with our interactive browser:

Features:

• 🔍 Hierarchical Navigation: Browse by Label → Split → Batch → Interaction
• 🎲 Random Sampling: Discover interactions with one-click random selection
• 📥 Download Interface: Download specific batches with size estimation and progress tracking
• 🎬 Video Viewer: Side-by-side participant videos with synchronized playback
• 📊 Data Analysis: Overview statistics and distribution plots
• 📁 File Management: Organize and preview audio, JSON, and NPZ files with expandable dropdowns

Download Options

We provide comprehensive download methods supporting all research scales and requirements:

Scale	Size	Method	Use Case	Script	Sampling
🔍 Single Example	~100MB	S3	Quick exploration, understanding data structure	download_s3.py	Auto-sample from preferred vendors
👥 Interaction Pair	~200MB	S3	Study conversational dynamics between participants	download_s3.py	Auto-detect conversation pairs
📂 Sample Set	~1GB	S3/HF	Initial prototyping, algorithm development	download_s3.py, download_hf.py	File selection or archive-based
🎯 Session Groups	~400MB	S3	Deep conversational context, session dynamics	download_s3.py	Auto-sample rich sessions
📦 Single Batch	~50GB	HF	Substantial local development, full exploration	download_hf.py	WebDataset tarball download
🗂️ Multiple Batches	~150GB+	HF	Training datasets, large-scale analysis	download_hf.py	WebDataset tarball download
🎯 Different Splits	Variable	HF	Cross-validation (train/dev/test, improvised/naturalistic)	download_hf.py	WebDataset tarball download
🌍 Whole Dataset	~27TB	HF	Complete research dataset, production systems	download_hf.py	WebDataset tarball download

🔍 S3 Download - Fine-grained exploration

Perfect for exploring individual interactions or specific file IDs. Downloads from S3 and automatically converts to consistent format (.wav, .mp4, .npz, .json).

# Initialize with configuration for cleaner setup
from seamless_interaction.fs import SeamlessInteractionFS, DatasetConfig

config = DatasetConfig(
    label="improvised", 
    split="dev", 
    preferred_vendors_only=True,
    local_dir=Path.home() / "datasets/seamless_interaction",  # note: we will automatically create the directory if it doesn't exist
)
fs = SeamlessInteractionFS(config=config)
# Or use defaults: fs = SeamlessInteractionFS()

file_ids = fs.sample_random_file_ids(num_samples=1)
fs.gather_file_id_data_from_s3(file_ids[0])
# Or specify exact file: fs.gather_file_id_data_from_s3("V00_S0809_I00000582_P0947")

# Files are organized as:
# local_dir/improvised/train/0000/0005/V00_S0809_I00000582_P0947.wav
# local_dir/improvised/train/0000/0005/V00_S0809_I00000582_P0947.mp4  
# local_dir/improvised/train/0000/0005/V00_S0809_I00000582_P0947.json
# local_dir/improvised/train/0000/0005/V00_S0809_I00000582_P0947.npz

For more details, please refer to the S3 Download Example.

📦 HuggingFace Download - Batch exploration

Ideal for downloading self-contained batches (~50GB each) for local exploration. Each batch contains complete interaction pairs.

from seamless_interaction.fs import SeamlessInteractionFS, DatasetConfig

# Initialize with configuration
config = DatasetConfig(label="improvised", split="dev")
fs = SeamlessInteractionFS(config=config)

# Sample set (~1GB) - Quick exploration on laptops
fs.download_batch_from_hf(batch_idx=0, archive_list=[0])

# Single batch (~50-100GB) - Substantial local development
fs.download_batch_from_hf(batch_idx=0)

For more details, please refer to the HuggingFace Download Example.

Working with Downloaded Data

from seamless_interaction.fs import SeamlessInteractionFS
import json
import numpy as np
import cv2
import librosa

# Load interaction data
def load_interaction_data(file_id):
    """Load all modalities for a given file ID."""
    
    fs = SeamlessInteractionFS()
    paths = fs.get_path_list_for_file_id_local(file_id)
    print(paths)
    
    data = {}
    for path in paths:
        if path.endswith('.mp4'):
            data['video'] = cv2.VideoCapture(path)
        elif path.endswith('.wav'):
            data['audio'], data['sample_rate'] = librosa.load(path, sr=48_000)
        elif path.endswith('.json'):
            with open(path) as f:
                data['json'] = json.load(f)
        elif path.endswith('.npz'):
            data['npz'] = np.load(path)
    
    return data

fs.download_archive_from_hf(
    archive=0,
    label="improvised",
    split="test",
    batch=0,
    local_dir=None,
    extract=True,
)
# Example usage
interaction = load_interaction_data("V01_S0223_I00000127_P1505")
print(f"Available feature keys: {list(interaction['npz'].keys())}")
print(f"Right hand pose data shape: {interaction['npz']['smplh:right_hand_pose'].shape}")

Basic Data Loading (HF + WebDataset)

from datasets import load_dataset

# configure
label = "improvised"
split = "dev"
batch_idx = 0
archive_list = [0, 1]

base_url = (
    f"https://huggingface.co/datasets/facebook/"
    f"seamless-interaction/resolve/main/{label}/{split}/"
    "{batch_idx:04d}/{archive_idx:04d}.tar"
)
urls = [base_url.format(batch_idx=batch_idx, archive_idx=archive_idx) for archive_idx in archive_list]
dataset = load_dataset(
    "webdataset", data_files={split: urls}, split=split, streaming=True
)

for item in dataset:
    break

isinstance(item["mp4"], bytes)
# True
item["npz"].keys()
# dict_keys(['boxes_and_keypoints:box', 'boxes_and_keypoints:is_valid_box', 'boxes_and_keypoints:keypoints', 'movement:EmotionArousalToken', 'movement:EmotionValenceToken', 'movement:FAUToken', 'movement:FAUValue', 'movement:alignment_head_rotation', 'movement:alignment_translation', 'movement:emotion_arousal', 'movement:emotion_scores', 'movement:emotion_valence', 'movement:expression', 'movement:frame_latent', 'movement:gaze_encodings', 'movement:head_encodings', 'movement:hypernet_features', 'movement:is_valid', 'smplh:body_pose', 'smplh:global_orient', 'smplh:is_valid', 'smplh:left_hand_pose', 'smplh:right_hand_pose', 'smplh:translation'])
item["json"].keys()
# dict_keys(['id', 'metadata:transcript', 'metadata:vad'])
item["wav"].keys()
# dict_keys(['path', 'array', 'sampling_rate'])

Check out the dataloader_webdataset.py script for more details.

🔍 Description

The seamless_interaction repository is split into several main components:

📊 Dataset

The repository provides comprehensive tools for downloading, processing, and utilizing the Seamless Interaction dataset for research and development. The dataset includes:

• Raw and processed multimodal data: Video, audio, transcripts, and annotations
• Precomputed features: Motion capture, facial keypoints, voice activity detection
• Metadata: Participant personality (BFI-2), interaction contexts, and relationships

📂 Repository Structure

seamless_interaction/
├── assets/               # Static assets for documentation
│   ├── banner.png
│   └── filelist.csv      # File list for the dataset
├── scripts/              # Example scripts for dataset usage
│   ├── dataloader_webdataset.py
│   ├── download_hf.py
│   ├── download_s3.py
├── src/seamless_interaction/  # Main package source code
│   ├── app/             # Data exploration application
│   ├── fs.py            # Filesystem interface for dataset access
│   ├── utils.py         # General utility functions
│   ├── constants.py     # Dataset constants and configuration
│   └── __init__.py
├── LICENSE              # CC-BY-NC 4.0 license
└── pyproject.toml       # Python package configuration

📦 Deep Dive into the Dataset

Dataset Structure

The Seamless Interaction Dataset is organized into two main categories/labels:

• Improvised: Interactions primarily based on predefined scenarios with guided prompts with at least one professional actor.
• Naturalistic: Prompted conversations that can be carried out by normal people.

seamless_interaction
├── improvised                # Interactions with guided prompts
│   ├── dev
│   │   ├── 1P-IS/            # First-party internal state annotations
│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
│   │   ├── 1P-R/             # First-party internal state rationale annotations
│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
│   │   ├── 3P-IS/            # Third-party internal state annotations
│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
│   │   ├── 3P-R/             # Third-party internal state rationale annotations
│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
│   │   ├── 3P-V/             # Third-party visual annotation
│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
│   │   ├── audio/            # Speaker-bleed denoised audio
│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.wav
│   │   ├── boxes_and_keypoints/
│   │   │   ├── box/          # Bounding boxes for each participant
│   │   │   ├── is_valid_box/ # Whether bounding boxes are valid
│   │   │   └── keypoints/    # Detected facial/body keypoints
│   │   ├── movement/         # Quantified Imitator movement features
│   │   │   ├── emotion_arousal/           # Arousal measures
│   │   │   ├── emotion_valence/           # Valence measures
│   │   │   ├── emotion_scores/            # Emotion detection scores
│   │   │   ├── expression/                # Facial expression parameters
│   │   │   ├── FAUToken/                  # Facial Action Unit tokens
│   │   │   ├── FAUValue/                  # Facial Action Unit values
│   │   │   ├── gaze_encodings/            # Eye gaze direction encodings
│   │   │   ├── head_encodings/            # Head position/rotation encodings
│   │   │   ├── frame_latent/              # Per-frame latent representations
│   │   │   └── is_valid/                  # Validity flags for extracted features
│   │   ├── smplh/            # SMPL-H body model parameters
│   │   │   ├── body-pose/    # Body pose parameters
│   │   │   ├── global_orient/ # Global orientation parameters
│   │   │   ├── is_valid/     # Valid frames indicators
│   │   │   ├── left_hand_pose/ # Left hand pose parameters
│   │   │   ├── right_hand_pose/ # Right hand pose parameters
│   │   │   └── translation/  # Global translation parameters
│   │   ├── transcript/       # Time-aligned speech transcription
│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.jsonl
│   │   ├── vad/              # Voice activity detection
│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.jsonl
│   │   └── video/            # Raw HD video recordings
│   │       └── V<vendor>_S<session>_I<interaction>_P<participant>.mp4
│   ├── test/                 # Test split with similar structure
│   └── train/                # Training split with similar structure
└── naturalistic/             # Spontaneous conversations
    ├── dev/                  # Same structure as improvised/dev
    ├── test/                 # Same structure as improvised/test
    └── train/                # Same structure as improvised/train

Each file is named according to a consistent convention:

• V<vendor_id>: Collection site/vendor identifier
• S<session_id>: Unique session identifier
• I<interaction_id>: Specific interaction within a session
• P<participant_id>: Individual participant identifier

Available Modalities and Features

Each interaction in the dataset includes:

Modality	Description	File Format	Sample Rate
🎥 Video	High-definition face-to-face footage	MP4 (H.264)	30/29.97 FPS, 1080p
🎙️ Audio	Denoised audio with separate channels	WAV	48kHz, 16-bit
📝 Transcript	Time-aligned speech transcription	JSONL	-
🏃 SMPL-H	3D body model parameters	NPY	30 Hz
🧠 Imitator Movement Features	Comprehensive quantified imitator movement data	NPY	30 Hz
📊 Annotations	Human-annotated behavioral data	JSON	-
🔊 VAD	Voice activity detection	JSONL	100 Hz
📦 Keypoints	Face and body keypoints	NPY	30 Hz

Annotation Types

The dataset includes several types of human annotations for rich behavioral analysis:

Annotation	Hours	Total Annotations	Mean # Tokens
1P-IS (1st-party internal state annotations)	1.1	751	5.8
1P-R (1st-party internal state rationale annotations)	1.1	751	10.2
3P-IS (3rd-party internal state annotations)	4.7	5132	5.2
3P-R (3rd-party internal state rationale annotations)	4.7	5132	11.3
3P-V (3rd-party visual annotation)	4.7	5132	14.6

Please refer to the technical report for a more detailed overview of annotations.

Movement/Imitator Feature Types

The movement directory contains rich behavioral features (output of the Imitator model):

Feature	Description
emotion_arousal	Arousal intensity measurements
emotion_valence	Valence (positive/negative) measurements
emotion_scores	Detected emotion categorical scores
expression	Parametric facial expression encodings
FAUToken/FAUValue	Facial Action Unit tokens and intensity values
gaze_encodings	Neural encodings of gaze direction
head_encodings	Neural encodings of head position and rotation
frame_latent	Per-frame latent representations
alignment_head_rotation	Head rotation data for temporal alignment
alignment_translation	Translation parameters for temporal alignment
EmotionArousalToken/EmotionValenceToken	Discretized emotion tokens
hypernet_features	Features from hypernetwork processing

Download Strategy Guide

We provide two complementary download methods optimized for different research workflows:

Method	Use Case	Best For	Download Size	Parallelization
S3 Direct	Fine-grained exploration	Individual interactions, interaction pairs	Per file (~100MB)	✅ Multiprocessing
HuggingFace Batches	Batch processing	Local dataset exploration, model training	~50-100GB per batch	✅ Multiprocessing

When to Use S3 Download

• Qualitative analysis: Examining specific interactions in detail
• Pair studies: Analyzing conversational dynamics between participants
• Feature exploration: Understanding data structure before large downloads
• Development: Testing code with minimal data

When to Use HuggingFace Download

• Model training: Need substantial training data
• Batch processing: Analyzing patterns across many interactions
• Local exploration: Want self-contained dataset on laptop/workstation
• Reproducible research: Ensure consistent data splits

Performance Optimization

# Optimal settings for different systems
config_default = DatasetConfig()  # auto-detects system resources
config_laptop = DatasetConfig(num_workers=4)      # Laptop/small workstation
config_workstation = DatasetConfig(num_workers=8) # High-end workstation
config_server = DatasetConfig(num_workers=16)     # Server/cluster node

# Memory-efficient batch processing
config = DatasetConfig(label="improvised", split="train")
fs = SeamlessInteractionFS(config=config)
for batch_idx in range(10):  # Process in chunks
    fs.download_batch_from_hf(batch_idx=batch_idx)
    # Process batch here...
    # Delete batch to free space if needed

Dataset Versions

The dataset is organized in self-contained batches for flexible exploration:

Split	Batches	Size per Batch	Total Size	Description
dev	5	~50GB	~500GB	Development/validation set
test	5	~50GB	~500TB	Hold-out test set
train	200+	~50GB	~20TB+	Full training data

Recommended Download Strategies

# Strategy 1: Quick Start (Laptop-friendly)
config = DatasetConfig(label="improvised", split="dev")
fs = SeamlessInteractionFS(config=config)
fs.download_batch_from_hf(batch_idx=0, archive_list=[0, 1, 2])  # ~6GB

# Strategy 2: Research Dataset (Workstation)
config = DatasetConfig(label="improvised", split="dev")
fs = SeamlessInteractionFS(config=config)
fs.download_batch_from_hf(batch_idx=0)     # Full dev set ~50-100GB

config = DatasetConfig(label="naturalistic", split="dev")
fs = SeamlessInteractionFS(config=config)
fs.download_batch_from_hf(batch_idx=0)   # Both interaction types

# Strategy 3: Production Training (Server/Cluster)
config = DatasetConfig(label="improvised", split="train")
fs = SeamlessInteractionFS(config=config)
for batch_idx in range(20):  # First 20 training batches (~1TB)
    fs.download_batch_from_hf(batch_idx=batch_idx)

File Format Specifications

Our data is stored in the following formats for optimal usability:

Format	Description	Usage
NPZ	NumPy array files	Efficient storage of numerical feature vectors, keypoints, and parameters
JSONL	JSON Lines	Time-aligned annotations with one event per line (e.g., transcripts, VAD)
JSON	JavaScript Object Notation	Structured metadata and annotations with timestamps
MP4	MPEG-4 Part 14	High-quality compressed video with H.264 encoding
WAV	Waveform Audio	Uncompressed audio for highest fidelity processing

🧪 Research Applications

The Seamless Interaction Dataset enables research across multiple domains:

Embodied AI and Virtual Agents

• Train agents that display natural gestures
• Model turn-taking dynamics and interaction rhythms
• Generate contextually appropriate responses to human behavior

Multimodal Understanding

• Analyze cross-modal correlations between speech, gesture, and expressions
• Extract behavioral patterns from large-scale interaction data
• Develop models to understand social dynamics

Human-Computer Interaction

• Design interfaces that respond to subtle human cues
• Improve telepresence technologies with better behavioral modeling
• Create more natural conversational agents

Animation and Content Creation

• Generate realistic human behaviors for animated characters
• Synthesize conversational dynamics for virtual production
• Create training data for digital human technologies

⚠️ Known Limitations and Noise in Metadata

Given the scale and complexity involved in collecting the Seamless Interaction dataset, there are several known limitations that we will address in our ongoing work, with improvements planned for in future versions:

Errors in Human-Based Time-Stamping

The core unit of the dataset is interactions. An interaction defines the active time during which a participant’s conversation and behavior can be linked to a pair of prompts. We have observed instances of misaligned time-stamps, including:

• Annotated start/end times may be too early or too late.
• Occasional misalignment between prompt text and spoken material.
• Ordering of prompts that may contain off-by-one errors.

Despite our efforts to automatically identify and correct these errors, approximately 10% of the interactions remain affected.

Time Stamping "Noise" in Moments of Interest (MOI)

While defining a MOI inherently involves some subjectivity, there are rare instances where:

• The described behavior only represents a subset of the observed behavior.
• The duration of the MOI does not fully capture the annotated behavior.

Incorrect Assignment of Participant IDs

In rare instances, we have observed:

• Duplicate participant identifiers being assigned to different individuals.
• The same individual being mapped to different identifiers.

Unreleased "Meta Time"

Currently, the dataset only contains active time segments - time in which two participants are actively responding to prompts. Meta time refers to the time between active segments in which participants are studying their new prompts, taking a break, etc. Meta time constitutes hundreds of hours in the raw collection and maybe be explored for future releases.

Variation in Recording Site Consistency

This multi-site project contains variation in:

• Recording quality, including issues like speaker bleed and participants staying in frame.
• Acting quality in Improvised segments.
• The likelihood of time-stamping errors.

All vendors met our technical requirements; however,there is noticeable variation in production quality across different sites.

🤝 Contributing

We welcome contributions from the research community! Here are some ways to contribute:

• Bug Reports & Feature Requests: Open issues on GitHub
• Dataset Improvements: Help enhance our preprocessing pipelines or annotations
• Model Contributions: Submit your models to our benchmarks
• Documentation: Improve our guides, tutorials, and API documentation
• Sample Code: Share example applications built with the dataset

Please see our CONTRIBUTING.md for detailed guidelines, code of conduct, and submission processes.

📄 License & Data Usage Policy

The Seamless Interaction Dataset is licensed under CC-BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0 International).

This means you are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material

Under the following terms:

• Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
• NonCommercial — You may not use the material for commercial purposes without explicit permission.

📑 Citation

If you use the Seamless Interaction Dataset in your research, please cite:

BibTeX

@article{seamless_interaction,
  title={Seamless Interaction: Dyadic Audiovisual Motion Modeling and Large-Scale Dataset},
  author={Vasu Agrawal and
        Akinniyi Akinyemi and
        Kathryn Alvero and
        Morteza Behrooz and
        Julia Buffalini and
        Fabio Maria Carlucci and
        Joy Chen and
        Junming Chen and
        Zhang Chen and
        Shiyang Cheng and
        Praveen Chowdary and
        Joe Chuang and
        Antony D'Avirro and
        Jon Daly and
        Ning Dong and
        Mark Duppenthaler and
        Cynthia Gao and
        Jeff Girard and
        Martin Gleize and
        Sahir Gomez and
        Hongyu Gong and
        Srivathsan Govindarajan and
        Brandon Han and
        Sen He and
        Denise Hernandez and
        Yordan Hristov and
        Rongjie Huang and
        Hirofumi Inaguma and
        Somya Jain and
        Raj Janardhan and
        Qingyao Jia and
        Christopher Klaiber and
        Dejan Kovachev and
        Moneish Kumar and
        Hang Li and
        Yilei Li and
        Pavel Litvin and
        Wei Liu and
        Guangyao Ma and
        Jing Ma and
        Martin Ma and
        Xutai Ma and
        Lucas Mantovani and
        Sagar Miglani and
        Sreyas Mohan and
        Louis-Philippe Morency and
        Evonne Ng and
        Kam-Woh Ng and
        Tu Anh Nguyen and
        Amia Oberai and
        Benjamin Peloquin and
        Juan Pino and
        Jovan Popovic and
        Omid Poursaeed and
        Fabian Prada and
        Alice Rakotoarison and
        Alexander Richard and
        Christophe Ropers and
        Safiyyah Saleem and
        Vasu Sharma and
        Alex Shcherbyna and
        Jia Shen and
        Jie Shen and
        Anastasis Stathopoulos and
        Anna Sun and
        Paden Tomasello and
        Tuan Tran and
        Arina Turkatenko and
        Bo Wan and
        Chao Wang and
        Jeff Wang and
        Mary Williamson and
        Carleigh Wood and
        Tao Xiang and
        Yilin Yang and
        Zhiyuan Yao and
        Chen Zhang and
        Jiemin Zhang and
        Xinyue Zhang and
        Jason Zheng and
        Pavlo Zhyzheria and
        Jan Zikes and
        Michael Zollhoefer
  },
  url={https://ai.meta.com/research/publications/seamless-interaction-dyadic-audiovisual-motion-modeling-and-large-scale-dataset/},
  year={2025}
}

🙏 Acknowledgments

This project was made possible thanks to contributions from:

• The thousands of participants who provided interaction data
• Our dedicated annotation and QA team
• Research collaborators from multiple institutions
• FAIR (Fundamental AI Research)
• The open-source community for valuable tools and libraries
• Our data collection partners across multiple sites
• Meta Reality Labs for supporting this research initiative